MACHINE LEARNING SOLUTIONS

Providing machine learning consulting services, we start with goal setting and analysis of business processes, then we help companies see machine learning capabilities matching their business goals.
By leveraging strong machine learning expertise, we handle all data-related processes, including data collection and preprocessing, to prepare datasets for effective modeling. We choose/create an optimal ML model, evaluating its accuracy to deliver production-ready solution tailored to specific business needs.

MACHINE LEARNING COMPETENCIES

Time Series Forecasting

With machine learning models trained on historical data, the tasks that demand forecasting, price predictions, seasonal fluctuations, and trends are solved much easier. Time series forecasting should become an integral part of the workflow for companies operating on markets with high volatility.

Time series classification

Confidence interval estimation

Stock market prediction

Time series clustering

NLP (Natural language processing)

CodeIT Machine Learning specialists create applications to recognize, process, and interpret unstructured written and oral natural language to extract meaningful information and value. We help companies from various industries improve their daily workflows using NLP outcomes.

The goal of anomaly detection is to identify unusual objects, events, or behavior in datasets that differ from the majority of data. The application of anomaly detection techniques is of great value in detecting intrusions, fraud, security issues, text data anomalies, health problems.

Unsupervised anomaly detection

Supervised anomaly detection

Semi-supervised anomaly detection

Technology stack

Tensor flow

Scikit Learn

keras

python

Matplotlib

Jupyter Notebook

Pandas

Numpy

Machine Learning Workflow

We can work at some stages in parallel

1

1.
Business task understanding

Define project requirements

Define task objective

2

2.
Data Exploration

Discover data insights

Plot graphs, visualization

Domain knowledge reception

3

3.
Data Preparation

Data cleaning

Feature extraction

Feature selection

Build pre-processing pipeline

4

4.
Modeling

Choose model

Define loss function and metrics

Parameter tuning

Being an iterative process, ML model development implies taking a step back to model accuracy or changes in the solution approach when getting results/performance scores.

5

5.
Evaluation / Validation

Performance scores estimation

Validation and test datasets evaluating

Cross-validation

We can work at some stages in parallel.

6

6.
Deployment

Continuous model delivery

Prepare production-ready solution

Metrics logging

Performance monitoring

Being an iterative process, ML model development implies taking a step back to model accuracy or changes in the solution approach when getting results/performance scores